Hyperspectral imagery can used to detect man made objects and classify backgrounds. The imagery is obtained by hyperspectral sensors, which represent a class of optical sensor that can collect a spectrum from each point in a scene. For remote sensing applications, they are typically deployed on either aircraft or satellites. Airborne hyperspectral imaging deals with imaging narrow spectral bands (e.g., approximately 10-20 nm) over a contiguous spectral range, and produces the spectra of all pixels in the scene. The data product from a hyperspectral sensor is typically a three-dimensional array or “cube” of data with the width and length of the array corresponding to spatial dimensions and the spectrum of each point as the third dimension.
In spectral space, anomalies are typically defined with reference to a model of the background. Background models can be developed adaptively using reference data from either a local neighborhood of the test pixel or a large section of the image. The typical approach to modeling the background probability density function is as a uni-modal multivariate Gaussian.
Statistical anomaly detectors can then classify hyperspectral image pixels into either background or anomaly clusters. The measured vector of radiances, for each image pixel, can be tested against the anomaly criterion typically described by a pair of statistical hypotheses: probability of the pixel to belong to the background and probability of the pixel to belong to the anomaly. Typically, when the anomalous spectral signature is unknown the Generalized Likelihood Ratio (GLR) test is used, which leads to an RX detection algorithm, such as the subspace RX (SSRX) algorithm.
In the prior art, a well-illuminated natural background typically satisfies the Gaussian model assumption and SSRX detection works reasonably well in that particular case. However, the presence of shadows violates the background model assumptions causing false alarms and missed detections of objects in shadows when the previously demonstrated algorithms are employed.
Accordingly, there remains a need in the art for a spectral anomaly detection method that enables detection of spectral anomalies in deep shadows and improved detection of spectral anomalies in poorly illuminated areas.